Intelligent fuse comprising monitoring module

Abstract

The invention relates to the technical field of state monitoring, in particular to an intelligent fuse comprising a monitoring module, and the fuse comprises a data analysis module, a steady state recognition module, a temperature analysis module, a coupling calculation module and a state calibration module. According to the method, time consistency verification and corresponding matching are carried out on temperature and current sampling, abnormal interference is eliminated in a stable current-carrying interval, a continuous sequence is formed, and a definite time relation is established between thermal response and a current-carrying state by combining analysis of accumulated deviation and change turning of the temperature along with time; the abnormal trend of slow evolution is amplified and presented, correction is carried out according to the direction consistency of multi-segment time difference change, progressive features before fusing are recognized in advance, early warning continuity and operation decision reliability are improved, misjudgment is reduced, continuous updating of state evaluation is supported, and stability of a control closed loop is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of state monitoring, in particular to an intelligent fuse containing a monitoring module. BACKGROUND

[0002] The technical field of state monitoring mainly involves continuously sensing, recording and judging key operating parameters such as current, voltage, temperature rise and breaking state of electrical equipment or system during operation. Its core tasks include real-time collection of abnormal state identification and fault symptom determination of electrical circuit working state, which usually relies on detection of current change, breaking state and connection integrity in the circuit to provide basic information support for equipment operation and maintenance and safety management, and is an indispensable technical direction in low-voltage distribution equipment and power terminal of power system.

[0003] Among them, the traditional intelligent fuse containing a monitoring module refers to a fuse that detects whether the fuse is on, whether the fuse body is broken and the size of the load current by introducing a detection point on the fuse body or the current-carrying circuit, cooperating with current detection elements, resistance temperature sensitive elements or mechanical contact structures, and outputting the detected state signals to external monitoring devices through wires or interfaces to realize the identification of the working state of the fuse. It usually completes the monitoring of the breaking state and operating state by arranging current transformers, shunt resistors or state contacts.

[0004] The existing state monitoring mainly relies on current conduction or single-point temperature rise change for judgment, and the detection logic is based on instantaneous threshold or contact state. The current fluctuation, load change and environmental interference in operation are easily confused and difficult to distinguish between normal fluctuation and abnormal trend. The lack of time correlation analysis leads to the disconnection of temperature and current-carrying state. The gradual degradation process of the fuse body is difficult to be continuously described, and it is easy to be in an unidentifiable state for a long time before breaking, causing early warning lag and passive operation and maintenance response, accumulation of safety risks and distortion of equipment reliability evaluation, and insufficient basis for management decision-making. SUMMARY

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide an intelligent fuse containing a monitoring module. The technical solution is as follows: On the one hand, an intelligent fuse containing a monitoring module is provided, which includes: A data analysis module acquires the temperature and current signals of the fuse body and performs analog-to-digital conversion, extracts temperature and current sample values and calculates sample period consistency parameters, time-correctly matches the temperature and current sample values, generates a synchronous monitoring sequence and transmits it to a steady-state identification module. A steady-state recognition module segments the current sample values by using a sliding window with a fixed time length based on the synchronous monitoring sequence, calculates the mean and standard deviation of the current sample values in each sliding window, removes the sliding window exceeding the current fluctuation threshold, generates a current stable section, and passes the current stable section to a temperature analysis module; A temperature analysis module extracts the temperature sample value corresponding to the time of the current stable section based on the current stable section, analyzes the time sequence variation characteristics of the temperature sample value, calculates the cumulative offset of the temperature variation, analyzes the turning point of the variation rate of the cumulative offset, generates a temperature variation point, and passes the temperature variation point to a coupling calculation module; A coupling calculation module performs time difference analysis on the starting time of the current stable section and the time corresponding to the temperature variation point, calculates the coupling offset, arranges a plurality of coupling offsets in time sequence of the current stable section, and generates a fuse monitoring result; A state calibration module extracts the variation direction of adjacent coupling offsets based on the coupling offset of the fuse monitoring result, judges the consistency of the variation direction, and performs state optimization calibration on the fuse monitoring result when the adjacent coupling offsets are in a continuous increasing relationship in time sequence, to generate an optimized fuse monitoring result.

[0006] As a further scheme of the application, the synchronous monitoring sequence includes a temperature time index, a current time index, and a temperature-current correspondence identifier, the current stable section includes a stable section starting time, a stable section ending time, and a current statistical feature identifier within the section, the temperature variation point includes a temperature turning time, a cumulative offset variation direction identifier, and a variation rate characteristic value, the fuse monitoring result includes a coupling offset time difference value, a section sequence number, and an offset arrangement sequence, and the optimized fuse monitoring result includes a calibrated offset sequence, a continuous increasing relationship identifier, and a state calibration result label.

[0007] As a further scheme of the application, the data analysis module includes: A signal acquisition sub-module acquires the temperature and current signals of the fuse, detects the amplitude variation of the two types of analog signals at the sampling port, performs analog-to-digital conversion, records the time markers of multiple sampling points according to a sampling clock, stores the converted temperature and current discrete value sequences in time sequence, and generates a temperature-current sample sequence; A consistency calculation sub-module calculates the continuous sampling interval based on the temperature-current sample sequence according to adjacent sampling points, performs difference operation on the temperature sampling interval set and the current sampling interval set, compares with a preset sampling period reference value, calculates a sampling interval deviation set, and obtains a sampling period consistency parameter; The time sequence matching sub-module performs time alignment judgment on the temperature sampling value and the current sampling value according to a corresponding time mark in the temperature current sampling sequence according to the sampling period consistency parameter, filters sampling pairs that meet the sampling period consistency parameter, and rearranges to generate a synchronous monitoring sequence.

[0008] As a further scheme of the present application, the sampling period reference value is determined by obtaining a system clock frequency and an analog-digital conversion trigger count parameter, and calculating a time length corresponding to a single sampling according to a conversion relationship between the clock frequency and the trigger count.

[0009] As a further scheme of the present application, the steady state recognition module comprises: The sequence receiving sub-module extracts continuous current sampling values based on the synchronous monitoring sequence, segments the current sampling values according to a fixed time length sliding window, records a sampling point number and a time index corresponding to each window, and generates a sliding window current sequence. The statistical calculation sub-module calculates a current mean value and a standard deviation value of a corresponding window based on the sliding window current sequence, analyzes a statistic quantity corresponding to each sliding window, and generates a window current statistic quantity set. The section generating sub-module compares a window current standard deviation with a set current fluctuation threshold value according to the window current statistic quantity set, eliminates windows exceeding the threshold value, retains a continuous window index interval that meets a condition, and merges adjacent window indexes to generate a current-carrying stable section.

[0010] As a further scheme of the present application, the current fluctuation threshold value is determined by quantitatively converting a standard deviation of a current sampling value in a sliding window according to a rated current-carrying range and a current fluctuation proportion obtained by a current sampling channel.

[0011] As a further scheme of the present application, the temperature analysis module comprises: The temperature mapping sub-module extracts a temperature sampling value corresponding to a time based on the current-carrying stable section, obtains a stable section time index and a temperature sampling sequence, sequentially aligns the temperature sampling values according to the time index, detects time stamp continuity and eliminates missing items, and generates a temperature time sequence sampling sequence. The cumulative offset sub-module calculates a temperature difference value at adjacent sampling times point by point based on the temperature time sequence sampling sequence, accumulates the difference value sequence in time sequence, monitors a numerical change amplitude in the accumulation process, and calculates a temperature change cumulative offset. The turning point extraction submodule calls the temperature change cumulative offset, calculates according to the offset change rate in a continuous time window, compares the change rate values of adjacent time windows and judges the sign change position, determines the time index at which the change rate changes from unidirectional change to reverse change, and generates a temperature change point.

[0012] As a further scheme of the present application, the coupling calculation module comprises: The time difference calculation submodule aligns the two types of time data according to the time stamp based on the starting time of the current-carrying stable section and the time corresponding to the temperature change point, performs item-by-item subtraction operation on the two types of time in the same section, judges the time sequence consistency and records the difference, and generates a section time difference; The coupling offset submodule obtains the number identification corresponding to the current-carrying stable section according to the section time difference value, performs linear mapping processing for each time difference value, associates the time difference value with the section number, and obtains a coupling offset; The monitoring result submodule calls the coupling offset, obtains a section ordering sequence based on the time sequence of the current-carrying stable section, performs rearrangement processing on a plurality of coupling offsets according to the ordering sequence, records the correspondence between the offset values after ordering and the section identification, and generates a fuse monitoring result.

[0013] As a further scheme of the present application, the state calibration module comprises: The offset direction submodule obtains an offset sequence arranged in time sequence based on the coupling offset of the fuse monitoring result, performs sign discrimination according to the difference between adjacent offset values, records the increase / decrease sign sequence corresponding to each adjacent position and aligns and arranges, and generates an offset change direction sequence; The consistency determination submodule obtains the direction symbol combination of consecutive adjacent positions according to the offset change direction sequence, performs consistency judgment operation on adjacent symbols, counts the length of the section where the consecutive increasing symbols appear, and arranges the identification of the increasing consistent section to obtain the increasing consistent section identification; The state calibration submodule calls the increasing consistent section identification, performs state calibration value update processing on the fuse monitoring result based on the coupling offset associated with the corresponding section, records the mapping relationship between the section calibration state and the time sequence, and generates an optimized fuse monitoring result.

[0014] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects: By time consistency check and corresponding matching of temperature and current sampling, abnormal interference is eliminated and continuous sequence is formed in stable current carrying interval, combining analysis of temperature accumulation offset and change turning point, clear time relationship between thermal response and current carrying state is established, slow evolving abnormal trend is amplified and presented, and progressive characteristics before fusing are identified in advance, improving early warning continuity and operation decision reliability, reducing misjudgment and supporting continuous update of state evaluation, and stable control closed loop is formed. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 It is a schematic diagram of the system of the present application; Figure 2 It is a schematic diagram of the system framework of the present application; Figure 3 It is a flow chart of the data analysis module in the present application; Figure 4 It is a flow chart of the steady state identification module in the present application; Figure 5 It is a flow chart of the temperature analysis module in the present application; Figure 6 It is a flow chart of the coupling calculation module in the present application; Figure 7 It is a flow chart of the state calibration module in the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the present application will be described below in combination with the drawings.

[0018] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. In fact, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0019] In the embodiments of the present application, "image" and "picture" can be used interchangeably, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0020] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0021] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0022] The embodiments of the present application provide a smart fuse containing a monitoring module, as shown in Figures 1-2 A smart fuse containing a monitoring module, as shown in the schematic diagram of a smart fuse containing a monitoring module, the fuse comprises: A data analysis module acquires the fuse body temperature and current signal and performs analog-to-digital conversion, extracts the temperature and current sample values and calculates the sampling period consistency parameters, performs time corresponding matching on the temperature and current sample values, generates a synchronous monitoring sequence and transmits it to a steady state identification module; A steady state identification module, based on the synchronous monitoring sequence, uses a fixed time length sliding window to segment the current sample values, calculates the mean and standard deviation of the current sample values in each sliding window and eliminates the sliding windows exceeding the current fluctuation threshold, generates a current-carrying stable section and transmits it to a temperature analysis module; A temperature analysis module, based on the current-carrying stable section, extracts the temperature sample value at the corresponding time, analyzes the time sequence change characteristics of the temperature sample value, calculates the cumulative offset of the temperature change, analyzes the turning point of the cumulative offset change rate, generates a temperature change point and transmits it to a coupling calculation module; A coupling calculation module, based on the time difference analysis of the start time of the current-carrying stable section and the time of the corresponding temperature change point, calculates the coupling offset and arranges multiple coupling offsets in time sequence of the current-carrying stable section, generates a fuse monitoring result; A state calibration module, based on the coupling offset of the fuse monitoring result, extracts the change direction of adjacent coupling offsets and judges the consistency of the change direction, when the adjacent coupling offsets are in continuous increasing relationship in time sequence, optimizes the state of the fuse monitoring result, generates an optimized fuse monitoring result.

[0023] The synchronous monitoring sequence comprises a temperature time index, a current time index, and a temperature-flow correspondence identifier. The current-carrying stable section comprises a stable section start time, a stable section end time, and a current statistical feature identifier within the section. The temperature change point comprises a temperature turning point, a cumulative offset change direction identifier, and a change rate characteristic value. The fuse monitoring result comprises a coupling offset time difference value, a section sequence number, and an offset arrangement sequence. The optimized fuse monitoring result comprises a calibrated offset sequence, a continuous increasing relationship identifier, and a state calibration result label.

[0024] Specifically, as shown in Figure 2 , 3 The data analysis module comprises: A signal acquisition sub-module acquires the temperature and current signals of the fuse element, detects the amplitude changes of the two types of analog signals at the sampling port, and performs analog-digital conversion. According to the sampling clock, the time markers of multiple sampling points are recorded, the converted temperature and current discrete value sequences are stored in time sequence, and a temperature and current sampling sequence is generated. In the power distribution monitoring circuit where the fuse is located, a high-precision Hall current sensor and a K-type thermocouple are configured as front-end sensing devices. The Hall current sensor is connected in series to the fuse circuit, and the sensitivity of the sensor is set to 30 millivolts per ampere; the K-type thermocouple probe is tightly attached to the center position of the fuse body shell, and is amplified by a signal conditioning circuit, the conditioning circuit is configured in linear output mode, and the gain is set so that the temperature sensitivity is 10 millivolts per degree Celsius (for example, 0.60 volts corresponds to 60.0 degrees Celsius). A 16-bit multi-channel analog-to-digital converter is started, the reference voltage is set to 3.30 volts, the sampling clock frequency is set to 10 Hz, and the corresponding ideal sampling period is 100 milliseconds. Channel 1 of the analog-to-digital converter is connected to the output end of the thermocouple signal conditioning circuit, and channel 2 is connected to the output end of the Hall current sensor (configured to output the effective value RMS or a low-pass filtered direct current signal). At the rising edge trigger time of each sampling clock, the analog-to-digital converter holds and performs quantization on the analog voltage amplitude of channels 1 and 2. Taking a certain time as an example, when the fuse current is 50.0 amperes, the analog voltage output by the Hall sensor is obtained by multiplying the current value by the sensitivity, i.e. 50.0 amperes multiplied by the sensitivity of 0.03 volts / ampere, equaling 1.50 volts. The analog-to-digital converter converts this voltage to a digital quantity, which is calculated by dividing the input voltage by the reference voltage and then multiplying by the upper limit of the quantization level, i.e. 1.50 / 3.30*65535=29789. When the fuse temperature is 60.0 degrees Celsius, the conditioning circuit output voltage is obtained by multiplying the temperature value by the sensitivity, i.e. 60.0*0.01=0.60 volts. The analog-to-digital converter converts it to a digital quantity, i.e. 0.60 / 3.30*65535=11915. At the same time, a 64-bit hardware timer of the microcontroller is called, which counts at a frequency of 1 kHz and has a resolution of 1 millisecond. In the analog-to-digital conversion completion interrupt service program, the current timer count value is immediately read as a time marker. Set the current time timer count value to 1000050, then assign it to the temperature value and current value collected at that time, respectively. Two independent circular buffer queues are opened in memory for storing temperature data structures and current data structures, respectively. The converted temperature discrete value sequence and current discrete value sequence, as well as their respective time marker sequences, are written into the dual-port random access memory in the order of time generation, forming the original temperature and current sampling sequence.

[0025] A consistency calculation sub-module, based on the temperature and current sampling sequence, calculates the continuous sampling interval according to the time of adjacent sampling points, performs difference operation on the temperature sampling interval set and the current sampling interval set, and compares with the preset sampling period reference value to calculate the sampling interval deviation set, and obtains the sampling period consistency parameter; The generated temperature current sample sequence is read, and 100 consecutive sample points are extracted as a calculation window. The sample period reference value is defined as 100 milliseconds. The time tags of the temperature sample sequence are traversed, and adjacent time difference operations are performed, that is, the time tag of the current sample point is subtracted from the time tag of the previous sample point to obtain a set of temperature sample intervals. Similarly, the same operation is performed on the current sample sequence to obtain a set of current sample intervals. Then, the difference value operation is performed on the same index position of the two interval sets, the absolute value of the difference value of the temperature sample interval and the current sample interval is calculated, and is recorded as the interval difference value. At the same time, the temperature sample interval is compared with the preset sample period reference value, and the absolute difference value between the two is calculated as the temperature deviation; the same comparison is performed on the current sample interval, and the absolute difference value between the two is calculated as the current deviation. Finally, the sample period consistency parameter is defined as the weighted sum of the three deviations. The calculation logic is: the interval difference value is multiplied by the weight 0.4, the temperature deviation is multiplied by the weight 0.3, the current deviation is multiplied by the weight 0.3, and the three products are added to obtain the consistency parameter. The setting of the weight here is based on the experimental conclusion that the inter-channel synchronization error has the greatest influence on power calculation, so a higher weight is given to the interval difference value. The specific calculation is shown in Table 1, and three consecutive sample points are selected for calculation.

[0026] Table 1: Sample period consistency parameter calculation table

[0027] As shown in Table 1, for index i+1, the temperature interval is 1000205-1000100=105 milliseconds, and the current interval is 1000203-1000102=101 milliseconds. The interval difference value is the absolute value of the difference between 105 and 101, which is equal to 4 milliseconds. The temperature deviation is the absolute value of the difference between the temperature interval 105 and the reference value 100, which is equal to 5 microseconds; the current deviation is the absolute value of the difference between the current interval 101 and the reference value 100, which is equal to 1 millisecond. The above values are brought into the weighted sum calculation logic, first calculate 4*0.4=1.6, then calculate 5*0.3=1.5, then calculate 1*0.3=0.3, and finally calculate 1.6+1.5+0.3=3.4. The calculation result 3.4 indicates that at index i+1, the time sequence jitter degree quantization value is 3.4.

[0028] The time sequence matching sub-module, according to the sample period consistency parameter, calls the corresponding time tag in the temperature current sample sequence, performs time alignment judgment on the temperature sample value and the current sample value, filters the sample pairs that meet the sample period consistency parameter, and rearranges to generate a synchronization monitoring sequence; According to the obtained consistency parameter sequence, the consistency constraint threshold is set to 5.0. The threshold setting is based on the fact that when the consistency parameter value exceeds 5.0, the power calculation error caused by the phase error will exceed 2%. Traverse the consistency parameter sequence to determine whether each consistency parameter meets the condition of being less than or equal to the constraint threshold. If the consistency parameter is less than or equal to 5.0, it is determined that the corresponding temperature sampling value and current sampling value meet the time alignment condition; if the consistency parameter is greater than 5.0, it is determined that there is a serious time deviation at this sampling time, and the rejection operation is performed. For the sampling points that meet the constraint, the original time mark is called. Taking the data with index i+1 in Table 1 as an example, the consistency parameter value is 3.4, which is less than the threshold 5.0, so the data is retained. Then, the arithmetic average operation is performed on the retained temperature time mark and current time mark to calculate the unified time axis coordinate. The calculation process is to add the temperature time mark 1000205 and the current time mark 1000203, the sum is 2000408, and then divided by 2 to get 1000204 milliseconds. Take the unified time coordinate 1000204 as the index key, and repackage the corresponding temperature sampling value and current sampling value into the synchronous monitoring sequence array. For the data with index i+2, according to Table 1, the consistency parameter value is calculated to be 1.9 (the interval difference value is 1*0.4+3*0.3+2*0.3=1.9), which also meets the condition of being less than 5.0. The unified time coordinate is the sum of 1000308 and 1000305 divided by 2, the result is 1000306.5 milliseconds, and the rounding processing is 1000307 milliseconds. The data is appended to the synchronous monitoring sequence in this time order. Perform this operation on all filtered sampling pairs to finally generate a synchronous monitoring sequence strictly monotonically increasing along the unified time axis.

[0029] Specifically, as shown in Figure 2 , 4 , the steady state recognition module includes: The sequence receiving sub-module extracts continuous current sampling values based on the synchronous monitoring sequence, segments the current sampling values according to a fixed time length sliding window, records the number of sampling points and time index corresponding to each window, and generates a sliding window current sequence. The generated synchronization monitoring sequence is called, which is stored in a specified address segment of the dual-port random access memory and contains pairs of temperature and current discrete values after time alignment processing. First, the time length parameter of the sliding window is set, which is set according to the observation requirements of current load stability. In order to effectively smooth short-term fluctuations and confirm that the current enters a sustained stable state, the fixed time length is set to 2000 milliseconds (2 seconds). At the same time, the step parameter of window sliding is set to 1000 milliseconds, that is, there is a 50% time overlap rate between adjacent windows, which ensures the continuous capture ability of the signal state change. The window index counter is initialized to 1, and the start time pointer is defined to point to the first time mark of the synchronization monitoring sequence. Taking the first window as an example, the first time mark of the synchronization monitoring sequence is read, and its value is set to 1000204 milliseconds. According to the time length of 2000 milliseconds, the cutoff time threshold of this window is calculated as 1000204+2000=1002204 milliseconds. Then, the current sampling values in the synchronization monitoring sequence are traversed, and all current data with time marks located in the interval of 1000204 milliseconds to 1002204 milliseconds are extracted into the first level cache. Since the sampling period is 100 milliseconds, this time interval theoretically contains 20 sampling points. The amount of data extracted is checked one by one, and after confirming that it is 20 points, the start time 1000204 milliseconds, the end time 1002204 milliseconds and the corresponding data number of this group of data are recorded, and it is marked as "window 1". After completing the data extraction of the current window, the sliding operation is performed. The start time pointer is moved back 1000 milliseconds, and the new start time becomes 1001204 milliseconds. The new cutoff time is calculated as 1003204 milliseconds. Repeat the above traversal and extraction actions to build "window 2". In this process, the latter half of the data of window 1 and the former half of the data of window 2 are completely the same, realizing the overlapping coverage of the data. Continue to execute the above segmentation operation until the remaining time of the synchronization monitoring sequence is less than 2000 milliseconds. Finally, all segmented current sampling value arrays and their corresponding time indexes are structured and stored, generating a sliding window current sequence.

[0030] The statistical calculation submodule calculates the mean and standard deviation values of the corresponding window current based on the sliding window current sequence, analyzes the statistics corresponding to each sliding window, and generates a set of window current statistics. The statistical calculation sub-module calculates the mean and standard deviation of the current in each window based on the sliding window current sequence, analyzes the statistics corresponding to each sliding window, and generates a window current statistics set. The statistical characteristics of each window in the sliding window current sequence are calculated in turn. First, initialize the accumulator variable and the square accumulator variable to zero. Take "window 1" as an example, which contains 20 current sampling values. Start the loop instruction and read the 20 values one by one. In each loop, the current value read is added to the accumulator variable, and the square value of the current value is added to the square accumulator variable. The current sampling values in window 1 are in a stable load state, with values fluctuating slightly around 50.0 amperes. After 20 loop operations, the sum of the accumulator variable is 1001.0 amperes. At this time, the division operation is called, and the sum 1001.0 / 20=50.05 amperes is obtained. Then, the current standard deviation is calculated to quantify the dispersion of the current. Using the "variance" calculation logic, first read the value of the square accumulator variable, set it to 50100.45. Then, use the formula "sum of squares minus (number of points multiplied by the square of the mean)" to calculate the sum of squares. The specific calculation is: 50100.45 - (20 *50.05 * 50.05) = 50100.45 - 50100.05 = 0.40. Then, 0.40 / 20=0.02. Finally, the square root instruction is called to calculate the square root of the variance 0.02, and the result 0.1414 amperes is obtained. This value is the standard deviation of window 1. Repeat the above calculation process for each sliding window to obtain the mean and standard deviation. For windows containing current impulse or fault waveforms, the standard deviation value will increase significantly due to the dramatic fluctuations in the data. For example, for "window 3", if the current fluctuates greatly during this period, the standard deviation calculated by the same process is 2.50 amperes. The calculated mean and standard deviation are written into the status register one by one to generate a window current statistics set containing multiple sets of statistical data. The specific calculation results are shown in Table 2.

[0031] Table 2: Window current statistics calculation results

[0032] As shown in Table 2, the standard deviations of window 1 and window 2 are low, indicating that the current is stable; the standard deviation of window 3 is significantly higher, indicating that the current has fluctuated greatly during this period; and the standard deviation of window 4 falls back although the mean increases, indicating that the current has entered a new stable state. This statistics set provides a quantitative basis for subsequent section division.

[0033] The segment generation sub-module compares the window current standard deviation with the set current fluctuation threshold according to the window current statistical quantity set, judges to eliminate the windows exceeding the threshold and keeps the index interval of the continuous windows satisfying the condition, merges the adjacent window indexes, and generates the current-carrying stable segment. Based on the window current statistical quantity set, the preset current fluctuation threshold is called to perform screening and discrimination. The setting basis of the threshold is the allowed ripple factor of the fuse under the rated load. The rated current of the monitored object is known to be 50 amperes, and the allowed normal background noise and ripple amplitude is one percent of the rated value, that is, 0.5 amperes. In order to prevent misjudgment, the current fluctuation threshold is set to 0.5 amperes. The statistical quantity data in Table 2 is traversed. First, for window 1, the current standard deviation is read as 0.14 amperes. 0.14 is compared with the threshold 0.5, and it is judged that 0.14 is less than 0.5, so it is determined that window 1 satisfies the stability condition, and its index and time interval are marked as “retained”. Then check window 2, its standard deviation is 0.16 amperes, which is also less than 0.5, and it is determined to be “retained”. Then check window 3, its standard deviation is 2.50 amperes, which is greater than 0.5, and it is determined that there is non-steady-state interference or transient process in the window, which is marked as “eliminated”. For window 4, the standard deviation is 0.15 amperes, which is less than 0.5, and it is determined to be “retained”. After completing the preliminary screening, the segment merging logic is executed, and all the window indexes marked as “retained” are scanned to find the continuous index sequence. In this example, window 1 and window 2 are continuous retained windows, and they have overlapping and continuous relationship on the time axis. The time interval of window 1 is 1000204 to 1002204 milliseconds, and the time interval of window 2 is 1001204 to 1003204 milliseconds. The two windows are merged into a continuous current-carrying stable segment, the start time of the segment is taken as the start time of window 1, that is, 1000204 milliseconds, and the end time is taken as the end time of window 2, that is, 1003204 milliseconds. Since window 3 is eliminated, the continuity is broken, and the merging of the current stable segment is ended. For window 4, although it is retained, since the previous window 3 is eliminated, window 4 will be treated as the starting point of the next independent stable segment. Finally, the current-carrying stable segment set composed of several discrete time periods is output, for example, the interval [1000204, 1003204] and the interval [1003204, 1005204]. These segments accurately eliminate the time of current fluctuation, and only retain the time segments in which the current is stable. The result shows that the screened data segment can truly reflect the resistance thermal effect characteristics of the melt under the steady state, and the interference on the resistance calculation is excluded.

[0034] Specifically, as shown in Figure 2 , 5 the temperature analysis module includes: The temperature mapping submodule extracts temperature sampling values corresponding to the time based on the current-carrying stable section, obtains a stable section time index and a temperature sampling sequence, sequentially aligns the temperature sampling values with respect to the time index, detects the continuity of the time stamp and eliminates missing items, and generates a temperature time sequence sampling sequence; The generated current-carrying stable section set is called, and one of the stable sections with a time span of 1000204 milliseconds to 1030204 milliseconds is selected as a processing object. The time length corresponding to this section is 30 seconds, and theoretically contains 301 sampling points. According to the start time index 1000204 milliseconds and the end time index 1030204 milliseconds of this section, the original temperature sampling sequence in the dual-port random access memory is addressed and searched. The corresponding temperature sampling values in this time range are extracted one by one. In the extraction process, strict time sequence continuity checking is performed, and the checking logic is: the hardware time mark of the i-th extraction point and the time mark of the i-1-th point are read, and the difference between the two is calculated. Given that the sampling clock is set to 100 milliseconds, if the calculated difference is strictly equal to 100 milliseconds, it is determined that the point is time-continuous and is retained; if the difference is greater than 100 milliseconds (for example, 200 milliseconds), it indicates that there is a frame loss or data recording phenomenon in the middle, and it is determined that there is a breakpoint at this position, and the incomplete data segment is marked as invalid or the missing values are linearly interpolated (in this embodiment, the missing items are removed, that is, if a breakpoint is found, the current sequence is truncated, and only the longest subsequence that meets the continuity requirement is output). After traversal search, 301 continuous temperature sampling values are successfully extracted. Taking the start time as an example, the temperature value corresponding to the time mark 1000204 milliseconds is 60.00 degrees Celsius; the temperature value corresponding to the time mark 1000304 milliseconds is 60.05 degrees Celsius; and so on, until the end time 1030204 milliseconds. Finally, this set of temperature data that has been time-aligned and verified for continuity is re-encapsulated to generate a temperature time sequence sampling sequence.

[0035] The cumulative offset submodule calculates the temperature difference between adjacent sampling times based on the temperature time sequence sampling sequence, calculates the cumulative offset of the temperature change by accumulating the difference value sequence in time sequence, and monitors the change amplitude of the cumulative value during the accumulation. The temperature time sequence is received, and a floating-point array is initialized to store the cumulative offset, with an initial value of 0. Starting from the second data point of the sequence, the algebraic difference between the current time temperature value and the previous time temperature value is calculated point by point. Taking the first three sampling points of the sequence as an example, the first sampling point (time 1000204 milliseconds) has a temperature of 60.00 degrees Celsius, which is taken as the reference point, and its cumulative offset is recorded as 0. The second sampling point (time 1000304 milliseconds) has a temperature of 60.05 degrees Celsius. The adjacent difference is calculated as 60.05-60.00=0.05 degrees Celsius. The difference is added to the offset of the previous time (0), and the current cumulative offset is 0.05. The third sampling point (time 1000404 milliseconds) has a temperature of 60.08 degrees Celsius. The adjacent difference is calculated as 60.08-60.05=0.03 degrees Celsius. The difference is added to the offset of the previous time (0.05), and the calculation process is 0.05+0.03=0.08. The above "difference-accumulation" operation is repeated for all remaining sampling points in the sequence. This process actually constructs a temperature change trend curve relative to the starting time, eliminating the influence of the absolute temperature value magnitude and retaining only the relative change amplitude. During the calculation process, the change amplitude of the cumulative value is monitored in real time. If it is found that the single-step increment exceeds the pre-set physical limit (for example, a single-step jump of more than 0.5 degrees Celsius, which is physically impossible in a 100-millisecond sampling interval), an abnormality flag is triggered. Finally, a sequence of temperature change cumulative offsets corresponding to the time index is generated, which clearly reflects the rising trajectory of the melt temperature during the transition from thermal equilibrium to a new steady state during current stabilization.

[0036] The turning point extraction submodule calls the temperature change cumulative offset, calculates the offset change rate within a continuous time window, compares the adjacent time window change rate values and determines the sign change position, determines the time index at which the change rate changes from unidirectional to reverse, and generates a temperature change point. The temperature change cumulative offset sequence is called, and the sliding time window length for calculating the change rate is set. To effectively capture the inflection point of temperature change and filter out high-frequency noise, the window length is set to 5 sampling points (i.e. 500 milliseconds). Two adjacent sliding windows are constructed: the current window and the subsequent window. First, calculate the offset change rate in the current window. The calculation method is: take the cumulative offset of the last point in the window minus the cumulative offset of the first point in the window, and then divide by the time span of the window. Set around a certain time, the data is shown in Table 3.

[0037] Table 3 Temperature offset and change rate calculation table

[0038] As shown in Table 3, first process "window A" (covering index i to i+5). The start offset of this window is 0.00, and the end offset is 0.01. The difference is calculated as 0.01 Celsius. The time span is 5 sampling points corresponding to 0.5 seconds. Therefore, the rate of change of window A is 0.02 Celsius per second. This value is close to 0, indicating that the temperature is in the initial thermal equilibrium or environmental noise state. Next, process the adjacent "window B" (covering index i+5 to i+10). The start offset of this window is 0.01, and the end offset is 0.06. The difference is calculated as 0.05 Celsius. The rate of change is 0.10 Celsius per second. This value is significantly greater than the rate of change of window A and the preset noise threshold (such as 0.05). Compare the rate of change of window A with the rate of change of window B in amplitude. The judgment logic detects that the rate of change has a stepwise rise, meaning that at the time corresponding to index i+5, the internal Joule heat finally reaches the shell temperature measurement point through the heat conduction of the arc extinguishing medium, breaking the original thermal equilibrium state of the shell. Record the time marker of index i+5, and define it as the "thermal response starting inflection point". This point represents the end point of the thermal conduction lag of the fuse under stable current loading. Perform this sliding comparison operation on the entire sequence, and finally output a set of temperature change points containing all identified turning time indexes.

[0039] Specifically, as shown in Figure 2 , 6 The coupling calculation module includes: The time difference calculation submodule aligns the two types of time data according to the time stamp based on the start time of the current-carrying stable segment and the time of the corresponding temperature change point, performs item-by-item subtraction operation on the two types of time within the same segment, judges the time sequence consistency and records the difference, and generates the segment time difference. The current-carrying stable section list is called, and the current to-be-processed "stable section 001" is locked. The start time attribute of the section is read, and the time value is the time when the current enters the steady state determined in the previous step, that is, 1000204 milliseconds. At the same time, the first effective thermal response start inflection point within or immediately after the stable section is retrieved from the generated temperature change point set. According to the principle of heat transfer delay, the temperature response always lags behind the current change, and the corresponding time mark is 1004504 milliseconds. This time point represents the time when the shell temperature responds significantly under the current excitation. Then, the timing alignment and difference operation instruction is executed. The thermal response start inflection point time 1004504 milliseconds is taken as the minuend, and the stable section start time 1000204 milliseconds is taken as the subtrahend, and the subtraction operation is performed. The calculation process is 1004504-1000204=4300. The numerical value has the dimension of milliseconds (i.e. 4.3 seconds), which represents the thermal conduction delay time (thermal propagation delay) of the fuse under the current working condition. In order to ensure the validity of the calculation result, logical verification is performed to determine whether the difference value is within the preset physical reasonable interval. It is known that the theoretical thermal conduction delay lower limit of the fuse of this type is 2000 milliseconds, and the upper limit is 10000 milliseconds. Comparing 4300 with 2000 and 10000, it is confirmed that 4300 is within the closed interval, and the calculation is valid. If the difference value is less than 0 or exceeds the range, an error flag will be triggered and the data set will be discarded. For the valid result, a key-value pair is constructed in the memory, with "stable section 001" as the key and "4300 milliseconds" as the value, and stored in the section time difference data set. The result quantifies the time lag feature in the electrical-thermal conversion process, and directly reflects the thermal conductivity of the arc-extinguishing medium around the fuse and the heat capacity state of the fuse itself.

[0040] The coupling offset submodule obtains the number identification of the corresponding current-carrying stable section according to the section time difference value, and performs linear mapping processing on each time difference value to associate and arrange the time difference value and the section number, to obtain the coupling offset value; Based on the time difference data set of the section, the time difference value 4300 milliseconds corresponding to the "stable section 001" is extracted. The preset "standard thermal response reference model" is called for numerical mapping. The setting of this reference model is based on the statistical data of a large number of new same-specification fuses, and it is determined that the standard thermal conduction delay time is 4000 milliseconds. The calculation logic of the coupling offset is defined as: the absolute value of the difference between the measured value and the reference value, divided by the reference value, to obtain the dimensionless relative deviation rate. The specific operation process is as follows: first, calculate the absolute deviation, 4300-4000=300 milliseconds. Then, perform the normalization division operation, 300 / 4000=0.075. This value 0.075 is the current coupling offset. In order to convert this continuous value into the identification required for hierarchical management, a nonlinear mapping coefficient is introduced. Set the mapping coefficient to 100, 0.075*100=7.5. At the same time, another historical section "stable section 002" is processed, and the recorded time difference value is 4500 milliseconds. Repeat the above process: 4500-4000=500, 500 / 4000=0.125, multiply by 100 to get the mapping value 12.5. The coupling offsets (7.5 and 12.5) calculated and the corresponding section numbers (section 001 and section 002) are associated and packaged. This process converts the physical quantity of the time dimension into an evaluation index reflecting the health status of the device. The closer the index is to zero, the closer the thermal response characteristics of the fuse are to the factory standard; the larger the index, the more likely it is that the thermal resistance of the heat conduction medium (arc extinguishing sand) has changed due to clumping or cavitation, or the fuse itself has aged and become thinner. The final coupling offset list provides direct data support for subsequent health degree sorting.

[0041] The monitoring result submodule calls the coupling offset, obtains a section ordering sequence based on the time sequence of the current-carrying stable section, performs rearrangement processing on multiple coupling offsets according to the ordering sequence, records the correspondence between the rearranged offset values and the section identifiers, and generates a fuse monitoring result. The coupling offset list containing multiple segment data is called, and a quick sort operation is performed. According to the size of the coupling offset value, the “stable segment 001” and the “stable segment 002” are arranged in ascending order. In this example, the offset of segment 001 is 7.5, and the offset of segment 002 is 12.5. It is determined that 7.5 is less than 12.5, so segment 001 is arranged at the beginning of the sequence, and segment 002 is arranged at the next position. The determination threshold system of the fuse health state is set: the first health threshold is 10.0, and the second warning threshold is 20.0. The sorted sequence is scanned in turn. For segment 001, its offset 7.5 is less than the first health threshold 10.0, and its state is marked as “healthy-optimal”. For segment 002, its offset 12.5 is greater than the first threshold 10.0 but less than the second threshold 20.0, and its state is marked as “attention-mild aging”. Based on the above determination, the final monitoring result table is generated. The table contains segment identification, calculated coupling offset and corresponding state evaluation. The specific data structure is shown in Table 4.

[0042] Table 4: Fuse thermal response monitoring result table

[0043] As shown in Table 4, by sorting the coupling offset and comparing it with the threshold, the classification evaluation of the fuse operating state is completed. The result table is uploaded to the host computer monitoring through the communication interface, and the historical trend document is established in the local memory. The monitoring result shows that although the fuse is currently in the normal working range, the data of segment 002 reveals that the thermal response characteristics fluctuate. This fine analysis based on real-time data enables the operation and maintenance personnel to grasp the degradation trend of the equipment before failure occurs, thereby realizing predictive maintenance.

[0044] Specifically, as shown in Figure 2 , 7 , the state calibration module includes: The offset direction submodule obtains the offset sequence arranged in time sequence based on the coupling offset of the fuse monitoring result, performs symbol discrimination according to the difference between adjacent offset values, records the increment / decrement symbol sequence corresponding to each adjacent position and aligns and organizes it, and generates the offset change direction sequence. The current-carrying stable segments generated in chronological order and the corresponding coupling offset data are called. In order to establish an effective trend analysis model, the five most recently generated stable segments are selected as the analysis window. The five segments are numbered in order of time stamp from far to near as stable segment 001, stable segment 002, stable segment 003, stable segment 004 and the latest stable segment 005 generated in the previous step. The corresponding coupling offset values are: stable segment 001 is 7.5, stable segment 002 is 7.9, stable segment 003 is 8.4, stable segment 004 is 8.8, and stable segment 005 is 8.5. The noise tolerance threshold is set to filter out the random errors inherent in the measurement. The setting of the threshold is based on the signal-to-noise ratio test of the combination of the Hall sensor and the thermocouple. In a constant working condition, long-time empty monitoring is carried out based on millisecond-level sampling, and the background noise standard deviation of the coupling offset is measured as 0.02. According to the 3σ criterion, the noise tolerance threshold is set to 0.06. Start performing adjacent numerical difference operation. First, calculate the difference between stable segment 002 and stable segment 001, that is, 7.9-7.5=0.4. Compare the absolute value 0.4 of the difference with the threshold 0.06, 0.4 is greater than 0.06, and the difference is positive, which is determined as an effective increase, and the symbol is recorded as "+1". Then calculate the difference between stable segment 003 and stable segment 002, 8.4-7.9=0.5, which is greater than 0.06, and is determined as an effective increase, and the symbol is recorded as "+1". Calculate the difference between stable segment 004 and stable segment 003, 8.8-8.4=0.4, which is greater than 0.06, and is determined as an effective increase, and the symbol is recorded as "+1". Finally, calculate the difference between stable segment 005 and stable segment 004, 8.5-8.8=-0.3. Its absolute value 0.3 is greater than 0.06, and the original difference is negative, which is determined as an effective decrease, and the symbol is recorded as "-1". Align the symbols obtained by the above calculation in chronological order to construct the sequence of offset change direction. The data structure of the sequence includes source segment index, target segment index, numerical difference result and direction discrimination symbol. In this embodiment, the generated sequence is [+1, +1, +1, -1]. The sequence directly reflects the dynamic evolution trajectory of the fuse thermal response characteristics in continuous time, and converts discrete numerical points into a direction set with vector characteristics.

[0045] A consistency determination sub-module obtains the direction symbol combination of the continuous adjacent positions according to the offset change direction sequence, performs a consistency judgment operation on the adjacent symbols, counts the length of the continuous increasing symbols, and arranges the identification of the increasing consistent segments to obtain the increasing consistent segment identification. The generated offset change direction sequence [+1, +1, +1, -1] is called, and the consistency scanning algorithm is started. The core parameter of this algorithm is the "consistency length threshold", which is set to 3. The determination of this parameter is derived from the accelerated aging experiment conclusion: under the action of a single aging mechanism (such as contact oxidation), the drift of thermal resistance value usually shows at least 3 consecutive monitoring periods of monotonic increase; if the consecutive increase period is less than 3, it is usually caused by random disturbance caused by environmental temperature fluctuation or contact surface fretting wear. Initialize the counter variable to 0 and set the starting pointer to point to the beginning of the sequence. Read the direction symbol bit by bit. The first bit symbol is "+1", the counter is 1, and the current value is 1. The second bit symbol is "+1", the counter is 1, and the current value is 2. The third bit symbol is "+1", the counter is 1, and the current value is 3. At this time, the counter value is detected in real time, and it is found that 3 is equal to the set threshold 3, which immediately triggers the segment locking mechanism. Backtracking index, confirming that this continuous increasing sequence covers the time range from stable segment 001 to stable segment 004, involving the coupling offset set {7.5, 7.9, 8.4, 8.8}. Pack this group of continuous segment identifiers and define it as "increasing consistent segment-001". Then, continue to read the fourth bit symbol "-1". Since the symbol is not equal to "+1", it does not meet the increasing condition, the counter is reset to zero, and the current continuity judgment process is terminated. Record the attribute information of "increasing consistent segment-001", including the starting segment number 001, the ending segment number 004, the value interval [7.5, 8.8] spanned and the duration 3. The judgment result shows that there is a persistent heat accumulation effect or resistance value climbing trend in the fuse inside the time window of stable segment 001 to stable segment 004, excluding the possibility of random disturbance, and passing the monotonic consistency test. Store the identified segment identifier in the pending queue for subsequent module feature calibration. The specific analysis data is shown in Table 5.

[0046] Table 5 Coupling offset trend analysis table

[0047] As shown in Table 5, steps 1 to 3 constitute a continuous increasing segment that meets the threshold requirement, although the single increment (such as 0.4, 0.5) does not reach the mutation alarm standard, but the continuous trend is successfully captured.

[0048] The state calibration submodule calls the increasing consistent segment identifier, updates the state calibration value of the fuse monitoring result based on the coupling offset associated with the corresponding segment, records the mapping relationship between the segment calibration state and the time sequence, and generates the optimized fuse monitoring result; The "incremental consistent segment-001" and its associated coupled offset data {7.5, 7.9, 8.4, 8.8} are read. In the regular monitoring logic, all these values are less than the first level health threshold 10.0, thus marked as "healthy-optimal" in the preliminary assessment. However, the intention is to identify potential early signs of aging through trend slope. First, the average growth rate of this segment is calculated. The calculation process is: take the last value 8.8 of this consistent segment minus the first value 7.5, the total increment is 1.3. Divide the total increment by the number of segment intervals 3, the average growth rate is 0.433. A "premonition sensitivity coefficient" is introduced as a calibration benchmark, which is set to 0.30. The setting of this value is based on the characteristic value of the thermal resistance change rate of the fuse metal melt at the initial stage of lattice creep. When the unit growth rate of the dimensionless offset exceeds 0.30, it indicates that irreversible microstructure damage has started inside the material. Compare the calculated average growth rate 0.433 with the benchmark value 0.30. Since 0.433 is greater than 0.30, it is determined that although the absolute value of this segment is still within the safe range, its deterioration speed has shown pathological characteristics. Based on this determination, a state override operation is performed. Locate the current end point of this consistent segment, which is the record item corresponding to the stable segment 004. Update the "healthy-optimal" state label of the stable segment 004 to "premonition-trend warning". For the stable segment 005 (value 8.5), since it is at the falling point after the interruption of the incremental trend, and the value 8.5 is still less than 10.0, its "healthy-optimal" state remains unchanged, but a label of "pay attention to the previous fluctuation" is added in the remarks column. Finally, the optimized fuse monitoring result list is generated, which not only contains the state classification based on instantaneous value, but also integrates the premonition information based on time series evolution. The result shows that by deeply mining the continuous small increments, the failure warning time point is successfully advanced, and the performance degradation dynamics trend is identified before the fuse shows significant aging characteristics (the value does not exceed the threshold).

[0049] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent fuse containing a monitoring module, characterized in that, The fuse comprises: The data analysis module collects the fuse body temperature and current signal and performs analog-digital conversion, extracts the temperature and current sampling value and calculates the sampling period consistency parameter, matches the temperature and current sampling value in time, generates a synchronous monitoring sequence and transmits it to the steady state identification module; The steady state identification module is based on the synchronous monitoring sequence, uses a fixed time length sliding window to segment the current sampling value, calculates the mean and standard deviation of the current sampling value in each sliding window and eliminates the sliding window exceeding the current fluctuation threshold, generates a current carrying stable section and transmits it to the temperature analysis module; The temperature analysis module extracts the temperature sampling value corresponding to the time based on the current carrying stable section, analyzes the time sequence change characteristics of the temperature sampling value, calculates the temperature change cumulative offset, judges the time position where the cumulative offset change rate turns, generates a temperature change point and transmits it to the coupling calculation module; The coupling calculation module performs time difference analysis based on the starting time of the current carrying stable section and the time corresponding to the temperature change point, calculates the coupling offset and arranges multiple coupling offsets in the time sequence of the current carrying stable section, and generates a fuse monitoring result.

2. The smart fuse with monitoring module according to claim 1, characterized in that, The synchronous monitoring sequence includes temperature time index, current time index, temperature flow corresponding identifier, the current carrying stable section includes stable section starting time, stable section ending time, section current statistical characteristic identifier, the temperature change point includes temperature turning time, cumulative offset change direction identifier, change rate characteristic value, and the fuse monitoring result includes coupling offset time difference value, section sequence number and offset arrangement sequence.

3. The smart fuse with monitoring module according to claim 1, characterized in that, The data analysis module comprises: The signal acquisition sub-module acquires the fuse body temperature and current signal, detects the amplitude change of the two types of analog signals at the sampling port and performs analog-digital conversion, records the time marks of multiple sampling points according to the sampling clock, stores the converted temperature and current discrete value sequence in time sequence, and generates a temperature current sampling sequence; The consistency calculation sub-module is based on the temperature current sampling sequence, calculates the continuous sampling interval according to the adjacent sampling point time, performs difference operation on the temperature sampling interval set and the current sampling interval set, compares with the preset sampling period reference value, calculates the sampling interval deviation set, and obtains the sampling period consistency parameter; The time sequence matching sub-module calls the corresponding time mark in the temperature current sampling sequence according to the sampling period consistency parameter, performs time alignment judgment on the temperature sampling value and the current sampling value, selects the sampling pairs meeting the sampling period consistency parameter, and rearranges to generate a synchronous monitoring sequence.

4. The smart fuse with monitoring module according to claim 3, characterized in that, The sampling period reference value is obtained by acquiring the system clock frequency and the analog-digital conversion trigger count parameter, calculating the time length corresponding to a single sampling according to the conversion relationship between the clock frequency and the trigger count to determine the sampling period reference value.

5. The smart fuse with monitoring module according to claim 1, characterized in that, The steady state identification module comprises: The sequence receiving sub-module extracts continuous current sampling value based on the synchronous monitoring sequence, segments the current sampling value according to a fixed time length sliding window, records the corresponding sampling point number and time index of each window, and generates a sliding window current sequence; A statistical calculation submodule calculates the mean and standard deviation of the current in each window based on the sliding window current sequence, analyzes the statistics corresponding to each sliding window, and generates a set of window current statistics; A section generation submodule compares the standard deviation of the window current with a set current fluctuation threshold, removes the windows exceeding the threshold, and retains the windows satisfying the condition, merges adjacent window indexes, and generates a current-carrying stable section.

6. The smart fuse with a monitoring module according to claim 5, characterized in that, The current fluctuation threshold is determined by quantitatively converting the standard deviation of the current sampling value in the sliding window based on the rated current range and the current fluctuation ratio obtained by the current sampling channel.

7. The smart fuse with monitoring module according to claim 1, characterized in that, The temperature analysis module includes: A temperature mapping submodule extracts the temperature sampling value corresponding to the time of the current-carrying stable section, obtains the stable section time index and the temperature sampling sequence, aligns the temperature sampling values in sequence according to the time index, detects the continuity of the time stamp and removes the missing items, and generates a temperature time sequence sampling sequence; A cumulative offset submodule calculates the temperature difference between adjacent sampling times based on the temperature time sequence sampling sequence, accumulates the difference sequence in time sequence, monitors the change amplitude in the accumulation process, and calculates the cumulative offset of the temperature change; A turning point extraction submodule calls the cumulative offset of the temperature change, calculates the offset change rate in a continuous time window, compares the change rate values of adjacent time windows and judges the sign change position, determines the time index at which the change rate changes from unidirectional to reverse, and generates a temperature change point.

8. The smart fuse with monitoring module according to claim 1, characterized in that, The coupling calculation module includes: A time difference calculation submodule aligns the two types of time data based on the start time of the current-carrying stable section and the time corresponding to the temperature change point, performs item-by-item subtraction operation on the two types of time in the same section, judges the consistency of the time sequence and records the difference, and generates a section time difference; A coupling offset submodule obtains the number identifier of the corresponding current-carrying stable section based on the section time difference value, performs linear mapping processing on each time difference value, associates the time difference value with the section number, and obtains the coupling offset; A monitoring result submodule calls the coupling offset, obtains the section ordering sequence based on the time sequence of the current-carrying stable section, performs rearrangement processing on multiple coupling offsets according to the ordering sequence, records the correspondence between the ordered offset values and the section identifiers, and generates a fuse monitoring result.

9. The smart fuse with monitoring module according to claim 1, characterized in that, The fuse further includes: A state calibration module extracts the change direction of adjacent coupling offsets based on the coupling offset of the fuse monitoring result, judges the consistency of the change direction, optimizes and calibrates the state of the fuse monitoring result when the adjacent coupling offsets are in a continuous increasing relationship in time sequence, and generates an optimized fuse monitoring result. The optimized fuse monitoring result includes a calibrated offset sequence, a continuous increasing relationship identifier, and a state calibration result label.

10. The smart fuse with a monitoring module according to claim 9, characterized in that, The state calibration module includes: The offset direction submodule obtains an offset sequence arranged in time sequence based on the coupling offset of the fuse monitoring result, performs sign discrimination according to a numerical difference between adjacent offsets, records an increment / decrement sign sequence corresponding to each adjacent position and performs alignment and arrangement, and generates an offset change direction sequence. The consistency judgment submodule obtains a direction sign combination of consecutive adjacent positions according to the offset change direction sequence, performs a consistency judgment operation on adjacent signs, counts a length of an appearance section of a continuously increasing sign, and arranges an identification of an increasing consistent section to obtain an increasing consistent section identification. The state calibration submodule calls the increasing consistent section identification, performs state calibration value update processing on the fuse monitoring result based on the coupling offset associated with the corresponding section, records a section calibration state and time sequence mapping relationship, and generates an optimized fuse monitoring result.

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